Instructions to use Anserwise/AWAXIS-KR-31B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Anserwise/AWAXIS-KR-31B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Anserwise/AWAXIS-KR-31B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Anserwise/AWAXIS-KR-31B")
model = AutoModelForImageTextToText.from_pretrained("Anserwise/AWAXIS-KR-31B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Anserwise/AWAXIS-KR-31B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Anserwise/AWAXIS-KR-31B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anserwise/AWAXIS-KR-31B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Anserwise/AWAXIS-KR-31B

SGLang

How to use Anserwise/AWAXIS-KR-31B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Anserwise/AWAXIS-KR-31B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anserwise/AWAXIS-KR-31B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Anserwise/AWAXIS-KR-31B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anserwise/AWAXIS-KR-31B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Anserwise/AWAXIS-KR-31B with Docker Model Runner:
```
docker model run hf.co/Anserwise/AWAXIS-KR-31B
```

AWAXIS-KR-31B

Overview

AWAXIS-KR-31B은 VIDRAFT Darwin AI 모델 교배/진화 플랫폼을 통해 생성된 한국어 특화 MoE 모델입니다. Darwin의 독자적인 **FFN-crossbreed 엔진(V8)**으로 한국어 특화 MoE 베이스(JDONE-Research/AIOne-Agent-52B-A36B-it)에 Opus-distill 추론 시그널(Anserwise/AWAXIS-Think-31B)을 교배 결합하였습니다.

Gemma-4 MoE 아키텍처(8 전문가 top-2 라우팅, 52B 총 / 36B 활성 파라미터, vision/audio 토큰 지원) 기반으로, 한국어 instruction following, 지식/문화 QA, 단계별 추론/수학 작업에 최적화되어 있으며, 한국어 4과목 종합 80.0% 성능을 검증했습니다.

AWAXIS-KR-31B is a Korean-focused MoE model (Gemma-4 family, 52B total / 36B active, 8 experts top-2 routing) created through the VIDRAFT Darwin AI Model Breeding/Evolution Platform. Built via Darwin V8 FFN-crossbreed engine, combining a Korean-specialized MoE base with Opus-distill reasoning signals through automated biological-inspired crossbreeding.

VIDRAFT Darwin AI 모델 교배/진화 플랫폼

**VIDRAFT Darwin**은 AI 모델의 **교배(Crossbreeding)와 진화(Evolution)**를 통해 새로운 고성능 모델을 자동 생성하는 플랫폼입니다. 생물학적 유전 원리에서 영감을 받아, 두 개 이상의 부모 모델에서 각각의 장점을 선택적으로 결합하여 자식 모델을 탄생시킵니다.

Darwin 교배/진화 핵심 기술

기술	설명
FFN Crossbreed Engine (V8)	부모 모델의 Feed-Forward Network(FFN) 레이어를 선택적으로 교차 결합하는 핵심 엔진. 어텐션/임베딩은 어머니(Mother)에서, FFN 시그널은 아버지(Father)에서 추출하여 블렌딩
Smart MRI (Model Resonance Imaging)	두 모델 간 레이어별 유사도/호환성을 분석하여 최적 교배 비율(alpha)을 자동 탐색하는 기술
Alpha Grid Search	교배 비율 alpha를 체계적으로 탐색하여 벤치마크 성능이 최대화되는 최적점을 발견 (자연선택 시뮬레이션)
Multi-Generation Breeding	1세대 교배 결과물을 다시 부모로 삼아 2세대, 3세대 교배를 수행하는 다세대 진화

이 모델의 Darwin 교배 과정

AWAXIS-KR-31B은 2세대(F2) 교배 모델입니다. 1세대에서 AWAXIS-Think-31B을 생성하고, 이를 다시 아버지로 삼아 한국어 MoE 어머니와 2세대 교배를 수행했습니다.

[1세대 교배] AWAXIS-Think-31B 생성
   Mother: TeichAI/gemma-4-31B-it-Claude-Opus-Distill-v2
   Father: google/gemma-4-31B-it
   --> Darwin FFN-crossbreed (alpha=0.1) --> AWAXIS-Think-31B

[2세대 교배] AWAXIS-KR-31B 생성 (이 모델)
   Mother: JDONE-Research/AIOne-Agent-52B-A36B-it (한국어 MoE)
   Father: AWAXIS-Think-31B (1세대 교배 결과물)
   --> Darwin FFN-crossbreed --> AWAXIS-KR-31B

이처럼 Darwin 플랫폼은 세대를 거듭할수록 능력이 누적 진화하는 다세대 교배(Multi-Generation Breeding)를 지원합니다.

왜 Darwin 교배인가?

기존 모델 합성 방식(단순 가중치 평균, SLERP, TIES 등)과 달리, Darwin 교배는:

생물학적 유전 모방: 어머니/아버지 역할을 명확히 분리하여 각 부모의 핵심 능력만 선택적으로 상속
FFN 선택적 주입: 어텐션(문맥 이해)은 어머니에서 100% 보존하고, FFN(지식/추론 패턴)만 아버지에서 교차 -> 능력 충돌 최소화
벤치마크 기반 자연선택: alpha grid search로 여러 자식 후보를 생성한 뒤, 실측 벤치마크로 최적 개체를 선택
다세대 진화: 1세대 결과를 부모로 재활용하여 능력 누적 (이 모델 = 2세대)

Model Lineage (모델 족보)

AWAXIS-KR-31B  (this model -- 2nd generation Darwin crossbreed)
|
+-- Mother (kept full, 100%)
|   JDONE-Research/AIOne-Agent-52B-A36B-it
|   -- Korean-specialized Gemma4 MoE 52B / A36B
|
+-- Father (FFN donor)
    Anserwise/AWAXIS-Think-31B  (1st generation Darwin crossbreed)
    |
    +-- Grandmother (kept full)
    |   TeichAI/gemma-4-31B-it-Claude-Opus-Distill-v2
    |   -- Claude Opus reasoning distill base
    |
    +-- Grandfather (FFN donor)
        google/gemma-4-31B-it
        -- Gemma-4 base

Direct Parents

Role	Model	Contribution
Mother (kept)	JDONE-Research/AIOne-Agent-52B-A36B-it	Korean capability, MoE routing, experts, attention, embeddings 100% preserved
Father (FFN donor)	Anserwise/AWAXIS-Think-31B	Opus-distill reasoning signal injected via dense FFN pathway

Paternal Grandparents

Role	Model
Grandmother	TeichAI/gemma-4-31B-it-Claude-Opus-Distill-v2
Grandfather	google/gemma-4-31B-it

Common ancestor: Google Gemma-4 architecture.

Datasets Used (활용 데이터셋)

본 모델의 한국어 능력 평가에는 K-AI Hub(NIA AI Hub) / K-AI Leaderboard(aihub.or.kr) 생태계의 표준 한국어 LLM 벤치마크 데이터셋을 활용했습니다.

Dataset	Domain	Source
KMMLU	Korean knowledge (45 subjects)	HAERAE-HUB/KMMLU
HAE_RAE_BENCH_1.1	Korean comprehension/culture (13 subsets)	HAERAE-HUB/HAE_RAE_BENCH_1.1
HRM8K	Korean math/reasoning (GSM8K Korean)	HAERAE-HUB/HRM8K
CLIcK	Korean culture-language	EunsuKim/CLIcK

Architecture


Class	`Gemma4ForConditionalGeneration` (multimodal: text + image + audio)
Parameters	52B total / 36B active (MoE, 8 experts, top-2 routing)
Layers	60
Hidden / Intermediate	5,376 / 21,504
Attention heads / head_dim	32 / 256
Vocab	262,144 (Gemma-4 tokenizer)
dtype	bfloat16

Measured Benchmarks

Benchmark	Setting	Score
Korean 4-Subject Composite (n=80, seed=42)	greedy	80.0%
-- KMMLU (knowledge)	20Q, greedy	70.0%
-- HAERAE-Bench (comprehension)	20Q, greedy	75.0%
-- HRM8K (math)	20Q, greedy	90.0%
-- CLIcK (culture-language)	20Q, greedy	85.0%
CLIcK (n=200)	greedy	88.0%

Intended Use

Korean instruction following
Knowledge/culture QA, reasoning/math
General Korean LLM tasks
Multimodal input (image-text-to-text) inherited from Gemma-4 base capability

Inference

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tok = AutoTokenizer.from_pretrained("Anserwise/AWAXIS-KR-31B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "Anserwise/AWAXIS-KR-31B",
    dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
    attn_implementation="eager",
)

msgs = [{"role": "user", "content": "한국의 외환위기 극복 과정을 단계별로 설명해 주세요."}]
text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
inp = tok(text, return_tensors="pt").to(model.device)
out = model.generate(**inp, max_new_tokens=2048, do_sample=False)
print(tok.decode(out[0][inp["input_ids"].shape[-1]:], skip_special_tokens=True))

License

This model includes Gemma-4 lineage weights and complies with the Gemma Terms of Use.

Acknowledgements

VIDRAFT -- Darwin AI Model Breeding/Evolution Platform
JDONE-Research for the Korean MoE base
TeichAI for the Opus-Distill base
Google DeepMind for Gemma-4

Built with the VIDRAFT Darwin AI Model Breeding/Evolution Platform -- FFN-crossbreed V8 engine. This is a 2nd-generation (F2) Darwin crossbreed model, created through automated biological-inspired crossbreeding that selectively combines the strengths of parent models. The Father (AWAXIS-Think-31B) was itself a 1st-generation Darwin crossbreed, demonstrating multi-generation evolution capability. Measured numbers above are exact; nothing inflated.